from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 34.0 | 45.945464 |
| daal4py_KNeighborsClassifier | 0.0 | 6.0 | 20.389947 |
| KNeighborsClassifier_kd_tree | 0.0 | 2.0 | 41.542006 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 0.0 | 33.014717 |
| KMeans_tall | 0.0 | 0.0 | 27.468542 |
| daal4py_KMeans_tall | 0.0 | 0.0 | 11.051374 |
| KMeans_short | 0.0 | 0.0 | 3.810694 |
| daal4py_KMeans_short | 0.0 | 0.0 | 1.912009 |
| LogisticRegression | 0.0 | 0.0 | 26.086144 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 5.944616 |
| Ridge | 0.0 | 0.0 | 11.442383 |
| daal4py_Ridge | 0.0 | 0.0 | 2.498267 |
| HistGradientBoostingClassifier | 0.0 | 6.0 | 35.438816 |
| lightgbm | 0.0 | 5.0 | 19.880914 |
| xgboost | 0.0 | 5.0 | 1.702277 |
| catboost | 0.0 | 5.0 | 7.042090 |
| total | 1.0 | 7.0 | 55.275298 |
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
scikit-learn's estimators vs daal4py¶reporting = Reporting(config_file_path="config.yml")
reporting.run()
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | brute |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.156 | 0.000 | 5.132 | 0.000 | -1 | 100 | NaN | NaN | 0.593 | 0.000 | 0.263 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 40.718 | 0.000 | 0.000 | 0.041 | -1 | 100 | 0.934 | 0.724 | 4.508 | 0.066 | 9.032 | 0.132 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.208 | 0.016 | 0.000 | 0.208 | -1 | 100 | 1.000 | 1.000 | 0.110 | 0.003 | 1.902 | 0.161 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.161 | 0.000 | 4.972 | 0.000 | 1 | 1 | NaN | NaN | 0.567 | 0.000 | 0.284 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 17.658 | 0.137 | 0.000 | 0.018 | 1 | 1 | 0.705 | 0.813 | 4.531 | 0.071 | 3.897 | 0.068 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.222 | 0.008 | 0.000 | 0.222 | 1 | 1 | 1.000 | 1.000 | 0.105 | 0.004 | 2.118 | 0.107 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.144 | 0.000 | 5.546 | 0.000 | 1 | 5 | NaN | NaN | 0.516 | 0.000 | 0.279 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 27.349 | 0.264 | 0.000 | 0.027 | 1 | 5 | 0.808 | 0.813 | 4.464 | 0.089 | 6.127 | 0.136 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.236 | 0.007 | 0.000 | 0.236 | 1 | 5 | 1.000 | 1.000 | 0.109 | 0.004 | 2.165 | 0.096 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.139 | 0.000 | 5.764 | 0.000 | -1 | 5 | NaN | NaN | 0.555 | 0.000 | 0.250 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 42.083 | 0.000 | 0.000 | 0.042 | -1 | 5 | 0.808 | 0.944 | 4.543 | 0.087 | 9.264 | 0.177 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.201 | 0.017 | 0.000 | 0.201 | -1 | 5 | 1.000 | 1.000 | 0.106 | 0.006 | 1.895 | 0.195 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.134 | 0.000 | 5.961 | 0.000 | -1 | 1 | NaN | NaN | 0.552 | 0.000 | 0.243 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 32.922 | 0.000 | 0.000 | 0.033 | -1 | 1 | 0.705 | 0.724 | 4.462 | 0.088 | 7.379 | 0.146 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.202 | 0.014 | 0.000 | 0.202 | -1 | 1 | 1.000 | 1.000 | 0.103 | 0.007 | 1.955 | 0.190 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.155 | 0.000 | 5.158 | 0.000 | 1 | 100 | NaN | NaN | 0.535 | 0.000 | 0.290 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 27.086 | 0.800 | 0.000 | 0.027 | 1 | 100 | 0.934 | 0.944 | 4.565 | 0.071 | 5.933 | 0.198 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.232 | 0.004 | 0.000 | 0.232 | 1 | 100 | 1.000 | 1.000 | 0.112 | 0.009 | 2.084 | 0.168 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.064 | 0.000 | 0.250 | 0.000 | -1 | 100 | NaN | NaN | 0.089 | 0.000 | 0.720 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 35.767 | 0.000 | 0.000 | 0.036 | -1 | 100 | 0.982 | 0.981 | 0.975 | 0.019 | 36.684 | 0.719 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.035 | 0.005 | 0.000 | 0.035 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 7.731 | 1.224 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.057 | 0.000 | 0.283 | 0.000 | 1 | 1 | NaN | NaN | 0.099 | 0.000 | 0.569 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 11.658 | 0.114 | 0.000 | 0.012 | 1 | 1 | 0.975 | 0.988 | 1.001 | 0.018 | 11.652 | 0.240 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.001 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.001 | 2.919 | 0.678 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.051 | 0.000 | 0.312 | 0.000 | 1 | 5 | NaN | NaN | 0.099 | 0.000 | 0.516 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.055 | 0.291 | 0.000 | 0.021 | 1 | 5 | 0.982 | 0.988 | 1.014 | 0.015 | 20.769 | 0.418 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.024 | 0.001 | 0.000 | 0.024 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.001 | 4.845 | 1.115 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.074 | 0.000 | 0.215 | 0.000 | -1 | 5 | NaN | NaN | 0.106 | 0.000 | 0.702 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 35.052 | 0.000 | 0.000 | 0.035 | -1 | 5 | 0.982 | 0.992 | 1.086 | 0.017 | 32.266 | 0.497 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.030 | 0.002 | 0.000 | 0.030 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 6.138 | 0.623 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.058 | 0.000 | 0.277 | 0.000 | -1 | 1 | NaN | NaN | 0.108 | 0.000 | 0.535 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 25.493 | 0.191 | 0.000 | 0.025 | -1 | 1 | 0.975 | 0.981 | 1.017 | 0.025 | 25.071 | 0.649 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.003 | 0.000 | 0.021 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 4.717 | 0.769 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.055 | 0.000 | 0.293 | 0.000 | 1 | 100 | NaN | NaN | 0.098 | 0.000 | 0.561 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 21.664 | 0.883 | 0.000 | 0.022 | 1 | 100 | 0.982 | 0.992 | 1.071 | 0.010 | 20.229 | 0.846 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.026 | 0.001 | 0.000 | 0.026 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.001 | 5.411 | 0.766 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | kd_tree |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.901 | 0.000 | 0.028 | 0.000 | -1 | 100 | NaN | NaN | 0.789 | 0.000 | 3.676 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.856 | 0.066 | 0.000 | 0.003 | -1 | 100 | 0.973 | 0.970 | 0.120 | 0.004 | 23.799 | 0.954 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 14.365 | 10.961 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.962 | 0.000 | 0.027 | 0.000 | 1 | 1 | NaN | NaN | 0.803 | 0.000 | 3.689 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.731 | 0.028 | 0.000 | 0.001 | 1 | 1 | 0.957 | 0.970 | 0.122 | 0.006 | 5.984 | 0.370 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 2.663 | 1.544 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.878 | 0.000 | 0.028 | 0.000 | 1 | 100 | NaN | NaN | 0.757 | 0.000 | 3.804 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.797 | 0.132 | 0.000 | 0.005 | 1 | 100 | 0.973 | 0.984 | 0.224 | 0.009 | 21.395 | 1.013 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 7.049 | 4.048 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.722 | 0.000 | 0.029 | 0.000 | 1 | 5 | NaN | NaN | 0.785 | 0.000 | 3.467 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.449 | 0.017 | 0.000 | 0.001 | 1 | 5 | 0.972 | 0.984 | 0.671 | 0.024 | 2.160 | 0.080 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 1.385 | 0.582 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.846 | 0.000 | 0.028 | 0.000 | -1 | 1 | NaN | NaN | 0.751 | 0.000 | 3.792 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.465 | 0.011 | 0.000 | 0.000 | -1 | 1 | 0.957 | 0.984 | 0.219 | 0.010 | 2.130 | 0.108 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 7.871 | 4.115 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.804 | 0.000 | 0.029 | 0.000 | -1 | 5 | NaN | NaN | 0.782 | 0.000 | 3.586 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.860 | 0.022 | 0.000 | 0.001 | -1 | 5 | 0.972 | 0.984 | 0.695 | 0.012 | 1.237 | 0.037 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 3.651 | 1.492 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.771 | 0.000 | 0.021 | 0.000 | -1 | 100 | NaN | NaN | 0.544 | 0.000 | 1.417 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.058 | 0.003 | 0.000 | 0.000 | -1 | 100 | 0.977 | 0.979 | 0.001 | 0.000 | 61.282 | 17.865 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.002 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 18.513 | 11.501 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.844 | 0.000 | 0.019 | 0.000 | 1 | 1 | NaN | NaN | 0.555 | 0.000 | 1.521 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.035 | 0.002 | 0.000 | 0.000 | 1 | 1 | 0.970 | 0.979 | 0.001 | 0.000 | 40.367 | 9.985 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.803 | 2.808 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.789 | 0.000 | 0.020 | 0.000 | 1 | 100 | NaN | NaN | 0.554 | 0.000 | 1.424 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.064 | 0.003 | 0.000 | 0.000 | 1 | 100 | 0.977 | 0.983 | 0.001 | 0.000 | 47.620 | 10.691 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 4.118 | 2.455 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.760 | 0.000 | 0.021 | 0.000 | 1 | 5 | NaN | NaN | 0.540 | 0.000 | 1.407 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.036 | 0.003 | 0.000 | 0.000 | 1 | 5 | 0.980 | 0.982 | 0.008 | 0.001 | 4.285 | 0.540 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 4.443 | 2.419 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.824 | 0.000 | 0.019 | 0.000 | -1 | 1 | NaN | NaN | 0.541 | 0.000 | 1.523 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.041 | 0.004 | 0.000 | 0.000 | -1 | 1 | 0.970 | 0.983 | 0.001 | 0.000 | 30.505 | 7.363 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 16.556 | 7.053 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.777 | 0.000 | 0.021 | 0.000 | -1 | 5 | NaN | NaN | 0.528 | 0.000 | 1.472 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.040 | 0.001 | 0.000 | 0.000 | -1 | 5 | 0.980 | 0.982 | 0.008 | 0.001 | 4.841 | 0.473 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 13.913 | 8.641 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 3 |
| max_iter | 30 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.701 | 0.000 | 0.685 | 0.000 | random | NaN | 30 | NaN | 0.336 | 0.0 | 2.084 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.278 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 7.328 | 2.313 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.001 | 0.000 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.836 | 4.135 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.680 | 0.000 | 0.706 | 0.000 | k-means++ | NaN | 28 | NaN | 0.325 | 0.0 | 2.092 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.276 | 0.000 | k-means++ | 0.001 | 28 | 0.001 | 0.000 | 0.0 | 6.723 | 2.356 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.002 | 0.000 | 0.000 | 0.002 | k-means++ | 1.000 | 28 | 1.000 | 0.000 | 0.0 | 7.464 | 3.804 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 8.212 | 0.000 | 2.923 | 0.000 | random | NaN | 30 | NaN | 4.107 | 0.0 | 1.999 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 12.409 | 0.000 | random | 0.001 | 30 | 0.002 | 0.000 | 0.0 | 5.807 | 2.352 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.014 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 8.152 | 4.228 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 8.450 | 0.000 | 2.840 | 0.000 | k-means++ | NaN | 30 | NaN | 4.440 | 0.0 | 1.903 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 12.242 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 5.739 | 2.483 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.014 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 7.052 | 3.869 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| algorithm | full |
| n_clusters | 300 |
| max_iter | 20 |
| n_init | 1 |
| tol | 0.0 |
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.334 | 0.0 | 0.010 | 0.000 | k-means++ | NaN | 20 | NaN | 0.059 | 0.0 | 5.654 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.143 | 0.000 | k-means++ | 0.000 | 20 | 0.002 | 0.001 | 0.0 | 3.014 | 0.801 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.672 | 5.534 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.109 | 0.0 | 0.029 | 0.000 | random | NaN | 20 | NaN | 0.154 | 0.0 | 0.705 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.149 | 0.000 | random | -0.001 | 20 | -0.001 | 0.001 | 0.0 | 2.813 | 0.783 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.002 | 0.0 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.790 | 4.802 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 1.211 | 0.0 | 0.132 | 0.000 | k-means++ | NaN | 20 | NaN | 0.279 | 0.0 | 4.333 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.004 | 0.0 | 4.531 | 0.000 | k-means++ | 0.329 | 20 | 0.333 | 0.002 | 0.0 | 2.060 | 0.358 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.009 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.246 | 3.965 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.372 | 0.0 | 0.430 | 0.000 | random | NaN | 20 | NaN | 0.625 | 0.0 | 0.595 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 4.607 | 0.000 | random | 0.387 | 20 | 0.330 | 0.002 | 0.0 | 1.887 | 0.519 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.009 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.057 | 3.964 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| penalty | l2 |
| dual | False |
| tol | 0.0001 |
| C | 1.0 |
| fit_intercept | True |
| intercept_scaling | 1 |
| class_weight | NaN |
| random_state | NaN |
| solver | lbfgs |
| max_iter | 100 |
| multi_class | auto |
| verbose | 0 |
| warm_start | False |
| n_jobs | NaN |
| l1_ratio | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 16.233 | 0.0 | [-0.07268544] | 0.000 | NaN | NaN | NaN | NaN | NaN | 3.148 | 0.0 | 5.156 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [43.09035402] | 0.000 | NaN | NaN | NaN | NaN | 0.507 | 0.000 | 0.0 | 0.764 | 0.349 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.15737081] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.000 | 0.0 | 0.343 | 0.327 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 1.347 | 0.0 | [1.54471968] | 0.001 | NaN | NaN | NaN | NaN | NaN | 1.127 | 0.0 | 1.195 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.003 | 0.0 | [79.93637679] | 0.000 | NaN | NaN | NaN | NaN | 0.250 | 0.004 | 0.0 | 0.671 | 0.096 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [13.57160195] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.174 | 0.072 | See | See |
All estimators share the following hyperparameters:
| value | |
|---|---|
| alpha | 1.0 |
| fit_intercept | True |
| normalize | deprecated |
| copy_X | True |
| max_iter | NaN |
| tol | 0.001 |
| solver | auto |
| random_state | NaN |
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.331 | 0.000 | 0.242 | 0.0 | NaN | NaN | NaN | 0.329 | 0.000 | 1.007 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.012 | 0.001 | 6.514 | 0.0 | NaN | NaN | 0.124 | 0.020 | 0.001 | 0.627 | 0.060 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 0.691 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.615 | 0.541 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.643 | 0.000 | 0.487 | 0.0 | NaN | NaN | NaN | 0.437 | 0.000 | 3.757 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 2.810 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 1.010 | 0.620 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.008 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.662 | 0.512 | See | See |
reporting_hpo = ReportingHpo(files=[
"results/benchmarking/sklearn_HistGradientBoostingClassifier.csv",
"results/benchmarking/xgboost_XGBClassifier.csv",
"results/benchmarking/lightgbm_LGBMClassifier.csv",
"results/benchmarking/catboost_CatBoostClassifier.csv"
])
reporting_hpo.run()